In modern medicine, local vessel diseases of patients, in particular stenoses, aneurysms and dissections, are usually diagnosed on the basis of 3-dimensional image data (3D image data) generated by computed tomography angiography (CTA). In order to acquire such 3D image data, the body region of interest of the patient is scanned in three dimensions by a computed tomography scanner after a contrast agent has been injected into the veins. The measured values acquired in the process are calculated and are made available for viewing or further evaluation as 3D image data or as 2D image data determined therefrom. Administering the contrast agent is necessary for there to be contrast between the blood vessels and the surrounding tissue. The lumen of the arteries, which is enriched by the contrast agent and therefore seems to be bright in the 3D CTA image data, can thus be evaluated and measured more easily.
It is typically a radiologist at a post-processing workstation who evaluates, analyzes and diagnoses vessel structures imaged in 3D image data. Various evaluation and imaging methods for vessel analysis are available these days. Inter alia, these include so-called maximum intensity projection (MIP) methods, volume rendering (VR) methods, multiplanar reconstruction (MPR) methods or curved planar reconstruction (CPR) methods, which are explained in more detail in, inter alia, the review article by Duddalwar V A, “Multislice CT angiography: a practical guide to CT angiography in vascular imaging and intervention”, The British Journal of Radiology, 77 (2004), pages S27-S38.
Key to the analysis of vessel diseases is the precise determination of 2D contours (contour lines) of the vessel structure imaged in the 3D image data, said contours for slice surfaces (cross-sectional surfaces) arranged orthogonally with respect to the central line being determined along a central line of the vessel structure. This is because only the precise knowledge of the 2D contours subsequently affords the possibility of exactly determining along the vessel the variables important for blood flow through said vessel: the minimum and maximum diameter of the vessel cross section, and the cross-sectional area. If, from the 3D image data for all central line points, corresponding 2D contours are determined, measured and plotted along the central line, the diameter or the cross-sectional area profile, for example, along the vessel can be illustrated graphically as a so-called vessel profile. Here, local minima along the profile of the profile curve indicate a stenosis and maxima indicate potential aneurysms. Moreover, the form of the disease can also be analyzed in more detail in the views; for example, the presence of calcifications or thrombotic structures can be inferred from the intensity of plaques and these can for example indicate the infarct risk of a patient.
These days, different methods for determining 2D contours in a vessel structure imaged in 3D image data are known.
A simple and often utilized algorithm for determining 2D vessel contours is the tracking of the outer edge of regions of related pixels, with the brightness of said pixels lying between an upper and a lower threshold. This approach lends itself in particular to the evaluation of CTA images since the brightness of the pixels is calibrated for every computed tomography scanner and can thereby be measured as an equipment-independent CT value.
The CT values indicate which tissue structure is imaged in the examined image region. Table 1 shows the identifying CT values for some tissue types. (cf. Table 1).
TABLE 1Tissue typeCT ValuesBone>250Thyroid 70 ± 10Liver65 ± 5Spleen45 ± 5Pancreas45 ± 5Kidney 40 ± 10Fat−90 ± 10Blood55 ± 5Gray matter30 ± 4
However, the quality of 2D contours determined by pure threshold-based evaluation methods in the 3D image data is very limited due to the following properties of CTA image data:    a) Arteries are often surrounded by similarly bright regions (i.e. by regions which have similar CT values), for example adjacent vessel structures, veins, bones and cartilage, calcifications, image artifacts, exsanguinations, etc.    b) As a result of image noise and an unevenly distributed contrast agent, there are intensity variations within the lumen enriched by the contrast agent.
If, for example, the lower threshold is set to be too low in such threshold-based evaluation methods, adjacent 3D image data regions merge and so the 2D contour determined thereby also includes these adjacent regions. This overestimates the vessel cross section which in individual cases falsifies vitally important measurement results. This disadvantageous effect particularly occurs when the spatial distance between the vessel structure of interest and the surrounding structures with similar CT values is less than the resolution of the imaging chain through the computed tomography scanner.
Alongside these methods, purely gradient-based evaluation methods are known and these detect the 2D contours solely on the basis of locally strongly pronounced changes in image values (grayscale values) without considering the absolute image values. However, these evaluation methods are very sensitive to image noise. Depending on the parameterization in the imaging and in the contrast agent distribution, there is different development of noise in the 3D image data. The image noise often generates pseudo-contours and so the purely gradient-based evaluation methods generally supply 2D contours of lower quality than threshold-based methods.
The document U.S. Pat. No. 6,782,284 B1 discloses a method for semi-automatic measurement of aneurysms and for planning surgical procedures connected to inserting synthetic stents into blood vessels. The method is based on evaluating provided 3D image data from medical imaging equipment, in which blood vessels are imaged. The method comprises the method steps of: identifying a blood vessel type, receiving vascular landmarks from an assigned user, extracting an orthogonal vessel plane, localizing a vessel center in the vessel plane, fitting vessel boundaries in the vessel plane and recursive repetition of the above method steps.
The document U.S. Pat. No. 6,643,533 B2 discloses a method for displaying tubular structures which are imaged in a medical 3D image data record. The method comprises the method steps of: determining a vessel central line; determining a vessel contour in a plane, with the plane being arranged orthogonally with respect to the vessel central line at a selected point of the vessel central line; determining the lengths of a multiplicity of segments which extend along the vessel structure and pass through the selected point; selecting a segment from the multiplicity of segments; and displaying an image plane which is defined by the selected segment and an axis which runs tangentially in respect of the vessel central line at the selected point.
The document DE 10 2007 019 554 A1 discloses a method based on volume data for the two-dimensional display of elongate lumen structures in a patient. The method comprises the following method steps: receiving tomographic volume data on the basis of a scan of the examination object and reconstructing detector data; segmenting at least one elongate hollow organ; automatically determining a start position and a start direction or receiving a manually defined start position or start direction; providing a multiplicity of 2D slice images along the elongate hollow organ; and emitting the 2D slice images of the hollow organ in the sequence of a virtual endoscopy.